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\title{PolaFusion at SemEval-2026 Task 9: Ensemble Transformers with Targeted Augmentation for Multilingual Polarization Detection}

\author{Abdullah Mohammad \\
  Delhi Skill and Entrepreneurship University (DSEU) \\
  Okhla, New Delhi, India \\
  \texttt{abdulla90t@gmail.com} \\}

\begin{document}
\maketitle

% ------------------------------------------------------------------
\begin{abstract}
% ------------------------------------------------------------------
We present \textbf{PolaFusion}, our system for SemEval-2026 Task~9, which requires detecting polarization in social media posts across 22 languages, classifying its type (Subtask~2), and identifying its rhetorical manifestation (Subtask~3). The task is characterized by severe and pervasive class imbalance across all three subtasks and all 22 languages. We address this through a combination of three strategies: a hierarchical gating architecture where a binary gatekeeper model gates two specialist classifiers trained exclusively on polarized content; an eight-model mega-ensemble combining five-fold mDeBERTa-v3-base \citep{he2021debertav3} and three-fold XLM-RoBERTa-large \citep{conneau2020unsupervised} with soft-vote probability aggregation; and a Macro-F1-aware augmentation strategy using Qwen3-235B \citep{qwen3} that generates synthetic minority-class examples only for language-label pairs that are both scarce and poorly learned. Throughout training, inverse-frequency class weighting within \texttt{BCEWithLogitsLoss} forces the model to attend proportionally to rare labels. Our system achieves official Macro-F1 scores of \textbf{0.800}, \textbf{0.576}, and \textbf{0.502} on Subtasks~1--3 respectively, outperforming the POLAR baseline \citep{naseem2025polar} by +0.040, +0.089, and +0.082 average Macro-F1 across languages. Our code is publicly available at \url{https://github.com/Abdullah4152/PolaFuse}.
\end{abstract}

% ------------------------------------------------------------------
\section{Introduction}
% ------------------------------------------------------------------

Polarization in online discourse operates through many distinct mechanisms: political sloganeering, ethnic dehumanization, religious vilification, gendered invalidation, and subtle forms of empathy denial that are no less corrosive for being harder to identify. Characterizing these phenomena computationally at scale---and across language boundaries---is both socially urgent and technically demanding \citep{sunstein2017republic, bail2018exposure}.

SemEval-2026 Task~9 \citep{semeval2026task9, naseem2025polar} structures this challenge as a three-layer prediction problem over 22 languages, evaluated with Macro-F1---a choice that forces systems to confront extreme label imbalance rather than ignore it.

That imbalance is the central difficulty of this task. In Subtask~1, the proportion of polarized posts ranges from under 11\% (Hausa) to over 90\% (Khmer). In Subtasks~2 and~3, certain labels---\textit{Dehumanization} in Odia, \textit{Lack of Empathy} in Hausa, \textit{Racial/Ethnic} in Bengali---appear in well under 2\% of training instances. Standard cross-entropy training on such distributions learns to ignore these labels entirely, collapsing to high accuracy but near-zero Macro-F1.

We respond to this with \textbf{PolaFusion}, a system designed from the ground up around the imbalance problem. We describe a hierarchical gating architecture, an eight-model cross-lingual ensemble, a targeted LLM-based augmentation strategy, and a cost-sensitive training objective that encodes class imbalance directly into the loss function. We show through careful ablation that each component contributes, and that some commonly used techniques (per-label threshold tuning) actively hurt generalization in this data regime.

% ------------------------------------------------------------------
\section{Related Work}
\label{sec:related}
% ------------------------------------------------------------------
\begin{figure*}[t]
  \centering
  \includegraphics[width=\textwidth]{fig_heatmap_all}
  \caption{Class imbalance heatmaps for all three subtasks and 22 languages. Each cell shows the positive-class proportion (\%). Darker red = more positive instances; near-white = severe label scarcity. Rows are aligned across panels so imbalance can be compared directly.}
  \label{fig:heatmap}
\end{figure*}

Online polarization detection builds on hate speech and toxic language classification. Shared tasks such as OffensEval \citep{zampieri2019semeval}, HaSpeeDe \citep{sanguinetti2020haspeede}, and SemEval tasks on misogyny identification \citep{yadav2022semeval} established paradigms for content-level hostility detection, while HateXplain \citep{mathew2021hatexplain} added explainability. Multilingual extensions \citep{ousidhoum2019multilingual, fortuna2021survey} showed that cross-lingual transfer partially bridges annotation gaps but struggles with culturally situated bias. On the modeling side, cross-validated ensembles of multilingual encoders consistently outperform single models by reducing correlated errors \citep{dietterich2000ensemble}. Data augmentation has evolved from EDA \citep{wei2019eda} and SMOTE \citep{chawla2002smote} toward LLM-based generation conditioned on label semantics. Our work integrates these strategies, targeting augmentation only where model performance warrants it.


% ── Fig 2: Architecture placeholder ──────────────────────────────────────
\begin{figure*}[t]
  \centering
  \includegraphics[width=\textwidth]{Gemini_Generated_Image_k4lah6k4lah6k4la.png}
  \caption{PolaFusion architecture. The Gatekeeper (Subtask~1) routes each post: non-polarized posts receive all-zero outputs immediately; polarized posts are passed to the Type and Manifestation specialists, which are trained exclusively on polarized instances.}
  \label{fig:arch}
  
\end{figure*}

% ------------------------------------------------------------------
\section{Task and Data}
% ------------------------------------------------------------------
% ── Fig 1: Heatmap ────────────────────────────────────────────────────────


% ------------------------------------------------------------------

Subtask~1 is binary classification over all posts: polarized (1) or non-polarized (0). Subtask~2 is multi-label classification into five types---\textit{Political}, \textit{Racial/Ethnic}, \textit{Religious}, \textit{Gender/Sexual}, and \textit{Other}---applied only to polarized posts. Subtask~3 is multi-label classification into six rhetorical manifestations---\textit{Stereotype}, \textit{Vilification}, \textit{Dehumanization}, \textit{Extreme Language}, \textit{Lack of Empathy}, and \textit{Invalidation}---likewise applied only to polarized posts. The underlying data is drawn from the POLAR benchmark \citep{naseem2025polar}, a multilingual, multicultural corpus covering 22 languages.

\paragraph{Class imbalance.}
Figure~\ref{fig:heatmap} presents a unified heatmap of the positive-class proportion across all three subtasks, all 22 languages, and all labels in the training split. For Subtask~1, the spread is extreme: Hausa has only 10.7\% polarized posts while Khmer reaches 90.8\%. In Subtask~2, \textit{Political} dominates most languages---Hindi reaches 74\%, Urdu 67\%---while \textit{Gender/Sexual} and \textit{Religious} sit below 15\% for the majority, and Italian has zero \textit{Political} training examples entirely. Subtask~3 exposes the most severe imbalance: \textit{Dehumanization}, \textit{Lack of Empathy}, and \textit{Invalidation} are near-absent in Hausa (3\%, 1\%, and 0\% respectively) and Odia (1\%, 2\%, 3\%). Italian, Polish, Russian, and Burmese show all-zero Subtask~3 cells, as these languages were not annotated for manifestations in the training split. This heatmap directly motivates our cost-sensitive loss and targeted augmentation strategy.
\vspace{-0.4em}
\section{System}
\label{sec:system}
% ------------------------------------------------------------------

Figure~\ref{fig:arch} provides an overview of the PolaFusion architecture.


\subsection{Hierarchical Gating}

The Subtask~2 and~3 specialists are trained exclusively on the \textit{polarized} portion of the training data, removing the diluting effect of all-zero label vectors from non-polarized instances. At inference, posts predicted as non-polarized by the gatekeeper receive all-zero Subtask~2 and~3 labels without invoking the specialists. A forced-argmax correction handles the residual edge case: if a post is predicted as polarized but all specialist probabilities fall below threshold, the label with the highest predicted probability is forced to 1, ensuring label consistency. We note that training the Subtask~2 and~3 specialists on the full dataset (including non-polarized instances) degraded performance in preliminary experiments, as the all-zero vectors dominated gradient updates and suppressed learning of minority labels.

\subsection{Mega-Ensemble}

PolaFusion combines two cross-lingual transformer families. mDeBERTa-v3-base \citep{he2021debertav3} employs disentangled attention over token content and relative position, benefiting morphologically complex languages. XLM-RoBERTa-large \citep{conneau2020unsupervised} offers larger model capacity trained on a significantly larger multilingual corpus, helping low-resource languages where mDeBERTa may lack coverage. We train five cross-validated folds of mDeBERTa and three of XLM-R-large, yielding eight models per subtask. The choice of five folds for mDeBERTa and three for XLM-R reflects a compute--diversity trade-off: mDeBERTa is lighter and benefits from additional fold diversity, while XLM-R-large is computationally expensive and already provides substantial capacity per fold. Final predictions are produced by averaging raw sigmoid probabilities across all eight models before thresholding---soft voting that smooths calibration without requiring learned weighting.

\subsection{Handling Class Imbalance}
\label{sec:imbalance}

We address imbalance through two complementary mechanisms applied at training time.

\paragraph{Cost-sensitive BCE loss.}
For each label $c$, we compute an inverse-frequency class weight:
\begin{equation}
    w_c = \frac{N}{K \cdot n_c}
    \label{eq:weight}
\end{equation}
where $N$ is the total training instances, $K=2$ for binary BCE labels, and $n_c$ is the positive count for class $c$. These weights are passed to \texttt{BCEWithLogitsLoss} via \texttt{pos\_weight}. For Hausa \textit{Invalidation} ($n_c=9$ out of 3{,}651 instances), $w_c \approx 202$; well-balanced labels receive $w_c \approx 1.0$. This handles the full imbalance spectrum without resampling or the interpolation artifacts of SMOTE \citep{chawla2002smote}, and avoids the tunable $\gamma$ of focal loss \citep{lin2017focal}.

\paragraph{Macro-F1-aware augmentation.}
We generate synthetic training examples using Qwen3-235B-A22B \citep{qwen3}, but only when two conditions hold simultaneously: the class is underrepresented (minority/majority ratio below 0.35 for Subtask~1, or label/max-label ratio below 0.25 for Subtasks~2 and~3) \textit{and} the observed per-label Macro-F1 on the development set falls below a threshold (0.60 for Subtask~1, 0.40 for Subtasks~2 and~3). The ratio thresholds correspond to the point below which inverse-frequency weighting alone fails to produce non-zero recall; the F1 thresholds mark the level below which a label is clearly being missed. These values were not extensively tuned and serve as conservative gates.

The joint condition concentrates augmentation where it can make a difference---a class that is rare but easy to learn does not benefit, while one that is rare \textit{and} being missed does. The number of synthetic examples is:
\begin{equation}
    s = \min\!\bigl(0.8 \times n_{\max},\; 2 \times n_c\bigr) - n_c
    \label{eq:augcount}
\end{equation}
where $n_{\max}$ is the most frequent label's count, capping synthetic output to prevent overrepresentation. For example, Hausa Subtask~1 (F1 = 0.760, ratio = 0.120) triggered generation of 412 synthetic polarized examples from few-shot seeds.
To illustrate, consider the following English \textit{Gender/Sexual} training 
instance and its Qwen3-generated synthetic variation:

\begin{quote}
\textbf{Original:} \textit{``She does look like a Fox News Stepford Wife.''} 
\textbf{[Political, Gender/Sexual]}\\[4pt]
\textbf{Synthetic:} \textit{``She really does resemble a brainwashed Fox News 
Barbie doll.''} \textbf{[Political, Gender/Sexual]}
\end{quote}

The synthetic post preserves the stance, target, and gendered register while 
substantially varying surface form, consistent with our prompt constraints.

Unlike template-based methods such as EDA \citep{wei2019eda}, augmentation is conditioned on observed development-set F1, ensuring synthetic effort is concentrated only where the model is actively failing. The LLM is prompted with the target language, label definition, and few-shot seeds from the training set, producing contextually appropriate synthetic posts. We note that the type and manifestation labels available for Subtasks~2 and~3 could in principle also inform Subtask~1 augmentation by conditioning synthetic examples on specific polarization types; we leave this as future work.


\subsection{Training Details}

All models are fine-tuned with AdamW \citep{loshchilov2018decoupled} at $2{\times}10^{-5}$ learning rate, batch size 16, maximum sequence length 128 tokens, up to 10 epochs with early stopping (patience 3). Language-stratified splits guarantee at least 10\% of each language's data in every validation fold, preventing empty folds for low-resource languages. Fixed global binarization thresholds: 0.50 (Subtask~1), 0.35 (Subtask~2), 0.30 (Subtask~3). All experiments run on NVIDIA Tesla T4 x2 GPUs via Kaggle Notebooks.

% ------------------------------------------------------------------
\section{Ablation Study}
\label{sec:ablation}
% ------------------------------------------------------------------

Figure~\ref{fig:ablation} summarizes the incremental Macro-F1 gains as each component is added to the system; points that degrade performance are marked $\downarrow$.

\paragraph{Ensembling dominates.}
The jump from a single mDeBERTa fold (0.350) to the full mega-ensemble (0.740) on Subtask~1 accounts for 0.390 Macro-F1 points---the largest single contribution. Combining one fold of each architecture already reaches 0.690, confirming that architectural diversity drives most of the gain. A single XLM-R fold (0.540) outperforms a single mDeBERTa fold (0.350), but the five-fold mDeBERTa ensemble (0.690) matches the three-fold XLM-R (0.680), illustrating how fold diversity compensates for per-model capacity.


\paragraph{Augmentation provides targeted gains.}
Macro-F1-aware augmentation adds 0.030 on Subtask~1, 0.009 on Subtask~2, and 0.037 on Subtask~3. Subtask~3 benefits most because \textit{Dehumanization} and \textit{Lack of Empathy} are near-absent in many languages; even a small number of synthetic examples provides enough positive signal to form a decision boundary. These gains are modest but consistent across subtasks and concentrated in long-tail labels.

\begin{figure}[t]
  \centering
  \includegraphics[width=\columnwidth]{fig5_ablation}
  \caption{Ablation study across all three subtasks. Each point shows the incremental Macro-F1 score as components are added. Red triangles ($\downarrow$) mark configurations where a component degraded generalization (threshold tuning); these are excluded from the final submission.}
  \label{fig:ablation}
  \vspace{-1.5em}
\end{figure}

% ── Merged results table with POLAR baseline (table*) ────────────────────
\begin{table*}[t]
\centering\small
\setlength{\tabcolsep}{3pt}
\begin{tabular}{l ccccc c ccc c cccc}
\toprule
 & \multicolumn{5}{c}{\textbf{Subtask 1}} & \phantom{}
 & \multicolumn{3}{c}{\textbf{Subtask 2}} & \phantom{}
 & \multicolumn{4}{c}{\textbf{Subtask 3}} \\
\cmidrule(lr){2-6}\cmidrule(lr){8-10}\cmidrule(lr){12-15}
\textbf{Lang} & \textbf{Acc} & \textbf{Prec} & \textbf{Rec} & \textbf{F1} & \textbf{BL} &
 & \textbf{F1-Mic} & \textbf{F1-Mac} & \textbf{BL} &
 & \textbf{F1-Mic} & \textbf{F1-Mac} & \textbf{BL} & \textbf{Exact} \\
\midrule
AM & 0.843 & 0.858 & 0.942 & 0.776 & .715 && 0.740 & 0.670 & .372 && 0.587 & 0.544 & .443 & 0.171 \\
AR & 0.834 & 0.813 & 0.818 & 0.833 & .796 && 0.636 & 0.620 & .486 && 0.646 & 0.602 & .390 & 0.508 \\
BE & 0.844 & 0.836 & 0.784 & 0.839 & .853 && 0.609 & 0.349 & .289 && 0.389 & 0.249 & .087 & 0.515 \\
DE & 0.720 & 0.718 & 0.685 & 0.719 & .671 && 0.567 & 0.562 & .408 && 0.505 & 0.490 & .349 & 0.406 \\
EN & 0.807 & 0.744 & 0.724 & 0.791 & .780 && 0.631 & 0.504 & .333 && 0.506 & 0.491 & .410 & 0.557 \\
FA & 0.846 & 0.874 & 0.925 & 0.787 & .842 && 0.720 & 0.605 & .463 && 0.586 & 0.458 & .200 & 0.363 \\
HA & 0.924 & 0.652 & 0.611 & 0.794 & .775 && 0.419 & 0.394 & .204 && 0.246 & 0.204 & .746 & 0.853 \\
HI & 0.913 & 0.935 & 0.965 & 0.813 & .738 && 0.874 & \textbf{0.790} & .791 && 0.782 & \textbf{0.759} & .235 & 0.303 \\
IT & 0.689 & 0.771 & 0.486 & 0.671 & .677 && 0.212 & 0.256 & .376 && --- & --- & --- & --- \\
KH & 0.920 & 0.937 & 0.978 & 0.703 & .659 && 0.842 & 0.699 & .627 && 0.721 & 0.400 & .610 & 0.619 \\
MY & 0.877 & 0.893 & 0.893 & 0.874 & .821 && 0.776 & 0.699 & .477 && --- & --- & --- & --- \\
NE & 0.909 & 0.913 & 0.905 & \textbf{0.909} & .880 && 0.766 & 0.774 & .722 && 0.683 & 0.645 & .131 & 0.539 \\
OR & 0.842 & 0.776 & 0.627 & 0.794 & .777 && 0.611 & 0.515 & .560 && 0.385 & 0.286 & .384 & 0.690 \\
PA & 0.768 & 0.737 & 0.812 & 0.768 & .790 && 0.553 & 0.472 & .365 && 0.519 & 0.504 & .456 & 0.386 \\
PO & 0.819 & 0.796 & 0.763 & 0.813 & .724 && 0.635 & 0.535 & .449 && --- & --- & --- & --- \\
RU & 0.835 & 0.730 & 0.709 & 0.801 & .746 && 0.638 & 0.565 & .590 && --- & --- & --- & --- \\
SP & 0.788 & 0.779 & 0.796 & 0.788 & .727 && 0.653 & 0.650 & .594 && 0.534 & 0.507 & .509 & 0.405 \\
SW & 0.771 & 0.791 & 0.738 & 0.771 & .757 && 0.664 & 0.411 & .442 && 0.568 & 0.547 & .221 & 0.422 \\
TE & 0.873 & 0.896 & 0.855 & 0.873 & .644 && 0.473 & 0.446 & .315 && 0.506 & 0.429 & .674 & 0.447 \\
TU & 0.794 & 0.792 & 0.821 & 0.793 & .696 && 0.658 & 0.586 & .471 && 0.609 & 0.515 & .769 & 0.412 \\
UR & 0.823 & 0.860 & 0.889 & 0.787 & .789 && 0.780 & 0.778 & .713 && 0.808 & \textbf{0.808} & .532 & 0.651 \\
ZH & 0.902 & 0.915 & 0.890 & \textbf{0.902} & .869 && 0.793 & \textbf{0.791} & .670 && 0.626 & 0.607 & .000 & 0.665 \\
\midrule
\textbf{Avg} & \textbf{0.834} & {0.819} & {0.801} & \textbf{0.800} & .760 && \textbf{0.648} & \textbf{0.576} & .487 && \textbf{0.567} & \textbf{0.502} & .420 & \textbf{0.495} \\
\bottomrule
\vspace{-1.5em}
\end{tabular}
\caption{Per-language results with POLAR baseline (BL) Macro-F1 from the official leaderboard \citep{naseem2025polar}. Subtask~1: Prec/Rec are for the polarized class. Subtask~3: dashes = languages not evaluated. Bold = highest F1-Mac per subtask.}
\label{tab:results}
\vspace{-1.5em}
\end{table*}


\paragraph{Threshold tuning overfits.}
Label-level and joint language+label threshold tuning both degrade performance ($\downarrow$0.015 and $\downarrow$0.008 respectively). The per-language-label validation counts are too small to estimate reliable thresholds; tuned values overfit to development-set noise. Fixed global thresholds generalize better and are used in the final system.

\paragraph{Forced argmax is necessary for Subtask~3.}
Without it, roughly 8\% of polarized posts receive all-zero manifestation predictions (F1~=~0 per instance). The correction recovers 0.015--0.023 Macro-F1 points and is especially important for low-resource languages where ensemble probabilities are poorly calibrated.



% ------------------------------------------------------------------
\section{Results}
\label{sec:results}
% ------------------------------------------------------------------



\paragraph{Subtask 1.}
The system achieves 0.800 average Macro-F1 and 0.834 average accuracy across 22 languages. Nepali (0.909), Chinese (0.902), and Burmese (0.874) are the strongest. Italian (0.671) is the weakest, with polarized-class recall of only 0.486; Italian polarization tends to be expressed through irony and understatement, which cross-lingual models trained on more direct expressions fail to transfer.
\paragraph{Subtask 2.}
The system averages 0.576 Macro-F1 and 0.648 Micro-F1 across 22 languages. Chinese (0.791) and Hindi (0.790) are the strongest. Italian (0.256), Bengali (0.349), and Hausa (0.394) are the hardest cases. The \textit{Other} and \textit{Racial/Ethnic} labels score lowest across virtually all languages, mirroring the near-empty cells visible in Figure~\ref{fig:heatmap}.

\paragraph{Subtask 3.}
Across 18 languages, the system achieves 0.502 Macro-F1 and 0.495 exact-match ratio. Urdu (0.808) and Hindi (0.759) are the strongest, reflecting their large and relatively balanced training sets. Hausa (0.204), Bengali (0.249), and Odia (0.286) are the hardest, each suffering from both data scarcity and near-absent minority labels. \textit{Dehumanization} and \textit{Lack of Empathy} remain the most difficult manifestations globally, with per-language F1 frequently below 0.30.

\paragraph{Comparison with POLAR baseline.}
Table~\ref{tab:results} compares PolaFusion against the official POLAR baseline \citep{naseem2025polar} (BL columns). On Subtask~1, PolaFusion outperforms the baseline in 17 of 22 languages (+0.040 average), with the largest gains on Telugu (+0.229) and Turkish (+0.098). On Subtask~2, we exceed the baseline in 17 of 22 languages (+0.089 average), led by Amharic (+0.298) and Burmese (+0.222). On Subtask~3, our system wins in 12 of 18 languages (+0.082 average), with notable gains on Hindi (+0.524) and Nepali (+0.513); however, the baseline substantially outperforms PolaFusion on Hausa ($-$0.541) and Turkish ($-$0.254).


% ------------------------------------------------------------------
\section{Discussion}
% ------------------------------------------------------------------

The results reveal a consistent hierarchy of difficulty: binary detection (F1~=~0.800) is substantially easier than type classification (0.576), which in turn is easier than manifestation identification (0.502). The persistent gap between Micro-F1 and Macro-F1 in Subtasks~2 and~3 confirms that common labels (\textit{Political}, \textit{Stereotype}) are learned well while rare ones (\textit{Racial/Ethnic}, \textit{Dehumanization}) remain systematically underpredicted. Our BCE weighting is effective because it operates during optimization rather than through data manipulation, scaling naturally to any degree of imbalance, though it cannot substitute for real signal when positive counts approach zero.

Italian and Hausa represent two structurally different failure modes. Italian's problem is linguistic register---sarcasm and politically coded vocabulary that cross-lingual models cannot transfer---suggesting culturally grounded few-shot augmentation as a remedy. Hausa's problem is representational: the language is underrepresented in pretraining corpora of both encoders, pointing toward continued pre-training or retrieval-augmented generation from native sources. The POLAR baseline comparison (Table~\ref{tab:results}, BL columns) further reveals that PolaFusion's gains are not uniform: the baseline outperforms our system on Hausa and Turkish in Subtask~3, suggesting its approach better handles certain manifestation distributions.


% ------------------------------------------------------------------
\section{Conclusion}
% ------------------------------------------------------------------

We presented PolaFusion, a system that treats class imbalance as the primary design constraint. Hierarchical gating, mega-ensemble soft voting, inverse-frequency BCE weighting, and Macro-F1-aware LLM augmentation together achieve 0.800, 0.576, and 0.502 Macro-F1 on the three subtasks, outperforming the POLAR baseline by +0.040, +0.089, and +0.082 average Macro-F1 respectively. Ensembling is the dominant gain; augmentation provides consistent but moderate improvements concentrated in data-scarce conditions; threshold tuning overfits at this data scale and should be avoided. Persistent failure on rare labels in low-resource languages remains the key open challenge.

% ------------------------------------------------------------------
\section*{Limitations}
% ------------------------------------------------------------------

Our final submission incorporates unlabeled test data into training via pseudo-labeling: the mega-ensemble's predictions on the official test set are treated as silver labels, and models are retrained on the union of training and pseudo-labeled data. This prevents computing unbiased held-out estimates, though official leaderboard scores remain unaffected. LLM augmentation may introduce distributional biases where few-shot seeds are limited. Fixed global thresholds are suboptimal for languages with extreme imbalance. The forced-argmax correction guarantees label consistency but can assign incorrect labels when confidence is uniformly low. We did not isolate augmentation and gating effects without the ensemble; a single-model ablation would clarify whether these components provide independent gains.

% ------------------------------------------------------------------
\section*{Acknowledgments}
% ------------------------------------------------------------------

We thank the SemEval-2026 Task~9 organizers for the dataset and evaluation platform. Experiments were run on NVIDIA Tesla T4 x2 GPUs via Kaggle Notebooks.

\bibliography{custom}
\clearpage
% ------------------------------------------------------------------
\appendix
% ==================================================================

% --- Color definitions ---
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\definecolor{headerbg}{RGB}{220, 230, 242}
 
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  basicstyle=\ttfamily\small,
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  columns=flexible,
  keepspaces=true,
  showstringspaces=false,
  % Highlight section tags like [Task], [Context], etc.
  moredelim=[s][\color{sectiontag}\bfseries]{[}{]},
  % Highlight placeholders like {TEXT}, {LABEL}, etc.
  moredelim=[s][\color{placeholder}\itshape]{\{}{\}},
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% --- tcolorbox style for individual prompt blocks ---
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% ---------------------------------------------------------------
\section{Augmentation Prompts}
% ---------------------------------------------------------------
 
We use three task-specific prompt templates for Qwen3-235B-A22B,
one per subtask. All prompts share the same structural skeleton
but differ in their label context block. Each prompt is called
with temperature~0.7, top-$p$~0.9, and a maximum of 128--150
tokens via the NVIDIA NIM API.
 
% ===========================
\subsection*{Subtask~1 — Polarization Detection}
% ===========================
 
\begin{tcolorbox}[promptbox, title=Prompt Template]
\begin{lstlisting}[style=promptstyle]
[Task]
Act as a linguistic expert in social media discourse.
Generate one synthetic variation of the text below
for a machine learning dataset.
 
[Constraints]
- Label Context: The text is {LABEL_DESCRIPTION}.
- Meaning: Preserve the same opinion, stance, and target.
- Linguistic Style: Maintain the original level of
  slang, intensity, and informal grammar.
- Content: Do NOT soften the language. Do NOT add
  apologies, disclaimers, or new entities.
- Output: Return ONLY the new sentence. No preamble.
 
[Text to Transform]
{TEXT}
 
[Augmented Result]
\end{lstlisting}
\end{tcolorbox}
 
\medskip
\noindent where \texttt{\{LABEL\_DESCRIPTION\}} takes one of two values
depending on the ground-truth label:
 
\begin{itemize}
  \item \textbf{Label~1} (polarized): \textit{``HIGHLY POLARIZED/BIASED.
        Keep the aggressive or divisive tone.''}
  \item \textbf{Label~0} (neutral): \textit{``NEUTRAL/NON-POLARIZED.
        Keep the objective or factual tone.''}
\end{itemize}
 
% ===========================
\subsection*{Subtask~2 — Polarization Type}
% ===========================
 
\begin{tcolorbox}[promptbox, title=Prompt Template]
\begin{lstlisting}[style=promptstyle]
[Task]
Act as a linguistic expert in social media discourse
specialized in {LANGUAGE}.
Generate one synthetic variation of the {LANGUAGE}
text below.
 
[Context]
This text is polarized based on: {LABELS}.
 
Requirements for labels:
- POLITICAL: Maintain political ideology, party bias,
  or political hostility.
- RACIAL/ETHNIC: Keep the bias/hostility toward the
  specific race, ethnicity, or nationality.
- RELIGIOUS: Preserve the hostility or blame directed
  at the religion or religious group.
- GENDER/SEXUAL: Maintain the bias or insult regarding
  gender or sexual orientation.
- OTHER: Preserve targets like media, economy, or
  technology not covered by identity labels.
 
[Constraints]
- Preserve the same meaning, stance, and target.
- Maintain the same level of aggression and tone.
- Do NOT sanitize, soften, or neutralize the language.
- Do NOT add or remove entities or targets.
- Do NOT change the polarization category.
- Do NOT use words like "please", "respectfully",
  or "balanced".
- Output ONLY the new sentence. No explanation.
 
[Text to Transform]
{TEXT}
 
[Augmented Result]
\end{lstlisting}
\end{tcolorbox}
 
% ===========================
\subsection*{Subtask~3 — Manifestation Identification}
% ===========================
 
\begin{tcolorbox}[promptbox, title=Prompt Template]
\begin{lstlisting}[style=promptstyle]
[Task]
Act as a linguistic expert in social media discourse
specialized in {LANGUAGE}.
Generate one synthetic variation of the {LANGUAGE}
text below.
 
[Context]
This text expresses: {LABELS}.
Requirement: {LABEL_HINTS}
 
[Constraints]
- Preserve the same meaning, stance, and target.
- Maintain the same level of aggression and intensity.
- Do NOT sanitize, soften, or neutralize the language.
- Do NOT add or remove entities or targets.
- Do NOT switch to another manifestation type.
- Do NOT use words like "please", "respectfully",
  or "balanced".
- Do NOT replace slurs with generic insults.
- Keep slang and informal grammar if present.
- Output ONLY the new sentence. No explanations.
 
[Text to Transform]
{TEXT}
 
[Augmented Result]
\end{lstlisting}
\end{tcolorbox}
 
\medskip
\noindent The \texttt{\{LABEL\_HINTS\}} placeholder is instantiated with a
definition for each manifestation class present in the sample:
 
\begin{itemize}
  \item \textbf{Stereotype} — use generalized claims about a group as
        inherent traits.
  \item \textbf{Vilification} — depict the target as immoral or corrupt.
  \item \textbf{Dehumanization} — portray the target as animals, objects,
        or less than human.
  \item \textbf{Extreme Language} — use highly aggressive or exaggerated
        expressions.
  \item \textbf{Lack of Empathy} — show indifference toward the target's
        suffering.
  \item \textbf{Invalidation} — dismiss the target's experiences or
        identity.
\end{itemize}

All three prompts use temperature 0.7, top-p 0.9, and a maximum of 128--150 tokens. The model is called via the NVIDIA NIM API.

% ------------------------------------------------------------------
\section{Augmentation Plans}
% ------------------------------------------------------------------

Tables~\ref{tab:aug1}--\ref{tab:aug3} list only the language--label pairs where both trigger conditions were met (ratio below threshold \textit{and} F1 below threshold) and synthetic examples were generated. Pairs not shown were not augmented. The full augmented datasets are available in our code repository.

\begin{table}[h]
\centering\small
\caption{Subtask~1 augmentation plan (triggered pairs only). Minority label: 1 = polarized, 0 = non-polarized.}
\label{tab:aug1}
\setlength{\tabcolsep}{5pt}
\begin{tabular}{lrrrr}
\toprule
\textbf{Lang} & \textbf{F1} & \textbf{Ratio} & \textbf{Label} & \textbf{Gen.} \\
\midrule
Hausa & 0.760 & 0.120 & 1 & 412 \\
Khmer & 0.614 & 0.101 & 0 & 642 \\
\bottomrule
\end{tabular}
\end{table}

\begin{table}[h]
\centering\small
\caption{Subtask~2 augmentation plan (triggered pairs only).}
\label{tab:aug2}
\setlength{\tabcolsep}{4pt}
\begin{tabular}{llrrrr}
\toprule
\textbf{Lang} & \textbf{Label} & \textbf{Count} & \textbf{F1} & \textbf{Ratio} & \textbf{Gen.} \\
\midrule
Amharic & Religious & 69 & 0.400 & 0.030 & 69 \\
Amharic & Gender/Sexual & 20 & 0.000 & 0.009 & 20 \\
English & Racial/Ethnic & 295 & 0.526 & 0.244 & 295 \\
English & Religious & 117 & 0.429 & 0.097 & 117 \\
English & Gender/Sexual & 75 & 0.286 & 0.062 & 75 \\
English & Other & 132 & 0.000 & 0.109 & 132 \\
Bengali & Racial/Ethnic & 26 & 0.000 & 0.022 & 26 \\
Bengali & Religious & 68 & 0.250 & 0.057 & 68 \\
Bengali & Gender/Sexual & 18 & 0.500 & 0.015 & 18 \\
Hausa & Gender/Sexual & 30 & 0.000 & 0.160 & 30 \\
Hausa & Other & 15 & 0.000 & 0.080 & 15 \\
Khmer & Racial/Ethnic & 103 & 0.267 & 0.022 & 103 \\
Punjabi & Racial/Ethnic & 106 & 0.125 & 0.191 & 106 \\
Polish & Gender/Sexual & 115 & 0.308 & 0.125 & 115 \\
Polish & Other & 163 & 0.375 & 0.177 & 163 \\
Chinese & Religious & 89 & 0.500 & 0.087 & 89 \\
Russian & Other & 83 & 0.000 & 0.170 & 83 \\
Swahili & Gender/Sexual & 164 & 0.286 & 0.063 & 164 \\
Swahili & Other & 583 & 0.357 & 0.224 & 583 \\
Odia & Racial/Ethnic & 125 & 0.400 & 0.240 & 125 \\
Odia & Gender/Sexual & 83 & 0.000 & 0.159 & 83 \\
Odia & Other & 91 & 0.000 & 0.175 & 91 \\
German & Gender/Sexual & 196 & 0.533 & 0.143 & 196 \\
Turkish & Other & 120 & 0.500 & 0.108 & 120 \\
Persian & Racial/Ethnic & 84 & 0.364 & 0.055 & 84 \\
Persian & Gender/Sexual & 207 & 0.435 & 0.136 & 207 \\
\bottomrule
\end{tabular}
\end{table}

\begin{table}[h]
\centering\small
\caption{Subtask~3 augmentation plan (triggered pairs only).}
\label{tab:aug3}
\setlength{\tabcolsep}{4pt}
\begin{tabular}{llrrrr}
\toprule
\textbf{Lang} & \textbf{Label} & \textbf{Count} & \textbf{F1} & \textbf{Ratio} & \textbf{Gen.} \\
\midrule
Telugu & Dehumanization & 62 & 0.000 & 0.095 & 62 \\
Amharic & Dehumanization & 460 & 0.508 & 0.241 & 460 \\
Bengali & Lack of Empathy & 66 & 0.000 & 0.078 & 66 \\
Bengali & Invalidation & 62 & 0.000 & 0.073 & 62 \\
Hausa & Lack of Empathy & 34 & 0.000 & 0.207 & 34 \\
Hausa & Invalidation & 9 & 0.000 & 0.055 & 9 \\
Khmer & Dehumanization & 85 & 0.333 & 0.018 & 85 \\
Khmer & Lack of Empathy & 765 & 0.482 & 0.161 & 765 \\
Khmer & Invalidation & 456 & 0.329 & 0.096 & 456 \\
Arabic & Invalidation & 288 & 0.375 & 0.218 & 288 \\
Chinese & Dehumanization & 226 & 0.581 & 0.167 & 226 \\
Odia & Dehumanization & 17 & 0.000 & 0.051 & 17 \\
Odia & Lack of Empathy & 39 & 0.000 & 0.117 & 39 \\
Turkish & Lack of Empathy & 237 & 0.318 & 0.221 & 237 \\
Turkish & Invalidation & 100 & 0.133 & 0.093 & 100 \\
Persian & Dehumanization & 149 & 0.316 & 0.075 & 149 \\
Persian & Lack of Empathy & 341 & 0.233 & 0.172 & 341 \\
Persian & Invalidation & 276 & 0.263 & 0.139 & 276 \\
\bottomrule
\end{tabular}
\end{table}

\end{document}